What is conversation intelligence?

Sales and customer conversations, conducted through phone calls, video meetings or chat platforms, offer a wealth of information. Conversation intelligence software captures these interactions. It then transcribes them and uses artificial intelligence (AI) technologies such as natural language processing (NLP) and machine learning (ML) to identify key moments, customer sentiment, pain points and other data.

Transforming raw conversations into structured, data-driven insights allows businesses to improve sales strategies and overall customer experience. And automating tasks like transcription, sentiment analysis and follow-up actions helps free sales and customer service teams to focus on higher-level relationships and driving business results.

Customer and sales conversations are loaded with valuable insights that can support business growth—but those potential insights might often go unnoticed. Conversation intelligence helps put them front and center, allowing businesses to uncover opportunities and areas for improvement. Organizations that adopt conversation intelligence tools can:

Understanding customer sentiment and pain points allows businesses to adapt their strategies to improve customer satisfaction and retention. For example, if sentiment analysis reveals that customers frequently express frustration about long wait times, a company can prioritize faster response times in their workflows.

Conversation intelligence highlights trends and patterns that might otherwise go unnoticed. For example, if multiple customer conversations reveal confusion about a product feature, the product team can use this insight to improve the feature or provide clearer documentation. Conversation insights also provide sales leaders with the data they need to optimize their sales process and allocate resources effectively.

By analyzing sales conversations and customer calls, sales teams can identify the behaviors and strategies that work best for their clients and refine strategies to close deals more effectively. For example, if data shows that top-performing sales reps spend more time discussing pricing transparency, sales leaders can encourage the entire team to adopt this approach.

Sales managers can use conversation intelligence to train new reps by sharing examples of successful calls and identifying areas for improvement.

While conversation intelligence offers many benefits, it does have some challenges and limitations. Organizations that adopt these platforms need to consider privacy issues, especially in regions with strict data protection laws.

The costs or complexity of instituting the tools and integrating them with other parts of the business may prove a challenge for some businesses. And because the technology is still developing, speech recognition and sentiment analysis might not always be 100% accurate. However, as conversation intelligence functionality continues to evolve, its applications are expanding across industries and its capabilities are improving.

Automating repetitive tasks such as transcription, note-taking and follow-up tracking saves time and promotes consistency. For example, a sales rep who no longer has to manually transcribe call recordings might instead focus on nurturing relationships with prospects.

While they might sound similar, conversation intelligence and conversational AI serve different purposes.

• Conversational AI refers to AI-powered systems, including chatbots and virtual assistants, that actively engage in real-time conversations with users. These tools simulate human interactions and take on basic tasks such as answering FAQs, scheduling appointments or troubleshooting recurring customer issues.
• Conversation intelligence, though, focuses on analyzing conversations in real time or just after they happen. It doesn’t engage with users directly but instead processes conversations to extract insights that can inform business strategies.

For example, imagine you’re on a company’s website. A chatbot (conversational AI) might pop up to answer your questions about pricing or product features. Later, the company’s sales team might analyze your recorded conversation by using conversation intelligence to identify your pain points and tailor their follow-up approach.

In this sense, conversational AI is the voice you interact with, while conversation intelligence is the brain working behind the scenes to make sense of those interactions. Conversational AI is about engaging in conversations, while conversation intelligence is about analyzing them.

Conversation intelligence relies on advanced technologies like natural language processing (NLP), machine learning (ML) and speech recognition to process and analyze conversation data. It includes:

Conversations are recorded from various sources, such as phone calls, video meetings (on platforms like Zoom or Microsoft Teams) or chat platforms.

Speech recognition technology transcribes spoken words into text, creating a searchable record that can then be analyzed.

NLP and ML algorithms review the conversation to identify key moments, customer sentiment action items and any relevant metrics. Some platforms offer real-time insights during live sales calls, helping sales reps adjust their approach immediately. And many offer automatic summaries of key moments or action items.

The software comes up with meaningful insights, such as recurring common objections, pricing discussions or customer pain points, which can be used to improve sales strategies or customer experience.

Conversation intelligence platforms can often integrate with CRMs like Salesforce, which can result in seamless data sharing and workflow automation.

Conversation intelligence platforms can process a wide range of data, including:

• Speech-to-text transcriptions: Spoken words from calls or meetings are converted into text, creating a searchable record of the conversation.
• Keywords and phrases: Platforms identify recurring terms or phrases, such as mentions of competitor names or product features.
• Sentiment analysis: Customer sentiment is analyzed to determine whether the tone of the conversation is positive, negative or neutral.
• Call duration and engagement: Metrics like how long a call lasts or how much time is spent on certain topics are tracked to note engagement.
• Action items: Specific tasks or commitments made during the conversation are flagged for follow-up.
• Customer intent: Platforms analyze language to determine the customer’s intent, such as making a purchase or exploring options.
• Objections and pain points: Common problems or challenges raised by customers are identified to help teams address them proactively.

While sales teams are a primary user group, conversation intelligence has applications across many industries. Areas that are using and benefitting from conversation intelligence include:

Sales reps use conversation intelligence to analyze their calls and refine their approach. For instance, a sales manager might notice that top performers consistently ask open-ended questions to uncover customer needs. Using this insight, the manager can create a playbook for team performance, helping new reps onboard faster and improve their performance. AI for sales strategies makes call summary automation a key part of the workflow, saving reps from manual note-taking.

Example: A sales rep reviews a recorded call and notices that they missed an opportunity to address a customer’s objection about pricing. With conversation intelligence, they can identify similar objections in other calls and prepare a more effective response for future interactions.

Call recordings are analyzed to improve agent performance and streamline workflows. For example, sentiment analysis might reveal that customers frequently express frustration during the billing process. This insight can prompt the contact center to simplify billing procedures or provide more training for agents. It can also help with the routing process and supervisory monitoring of issues.

Example: A contact center manager uses dashboards to track agent performance and notices that one agent consistently resolves issues faster than others. By analyzing their calls, the manager identifies best practices that can be shared with the entire team.

Conversation intelligence can be used to track customer sentiment and identify potential risks or common points of dissatisfaction so that the customer success team can proactively address the issues before they escalate.

Example: A customer success manager reviews a call summary and notices that a long-time customer expressed frustration about a recent update. The manager schedules a follow-up call to address their concerns and provide a solution, improving retention.

Businesses analyze customer interactions to optimize pricing strategies and improve the shopping experience. For instance, if customers frequently ask about discounts or promotions, the company might adjust its pricing strategy to better meet demand.

Example: An e-commerce manager reviews chat transcripts and notices that customers often abandon their carts after asking about shipping costs. The company decides to offer free shipping on orders over a certain amount, leading to increased conversions.

Providers analyze patient interactions to improve communication and ensure compliance with regulations. Conversation intelligence can flag instances where patients express confusion about their treatment plans, prompting providers to offer clearer explanations.

Example: A healthcare administrator uses conversation intelligence to identify common questions patients ask during consultations. This insight is used to create an FAQ document that improves patient understanding and reduces follow-up calls.